CN112185186B - Pronunciation correction method and device, electronic equipment and storage medium - Google Patents

Pronunciation correction method and device, electronic equipment and storage medium Download PDF

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CN112185186B
CN112185186B CN202011069449.5A CN202011069449A CN112185186B CN 112185186 B CN112185186 B CN 112185186B CN 202011069449 A CN202011069449 A CN 202011069449A CN 112185186 B CN112185186 B CN 112185186B
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pronunciation
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CN112185186A (en
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顾宇
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Beijing Youzhuju Network Technology Co Ltd
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    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B5/00—Electrically-operated educational appliances
    • G09B5/04—Electrically-operated educational appliances with audible presentation of the material to be studied
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/02—Feature extraction for speech recognition; Selection of recognition unit

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Abstract

The application discloses a pronunciation correction method, a pronunciation correction device, electronic equipment and a storage medium. The method comprises the following steps: extracting acoustic features irrelevant to a speaker from voice information of a user to be tested; obtaining pronunciation characteristic information of a user to be tested according to the characteristic information of the user to be tested, which is irrelevant to a speaker; and determining a pronunciation correction mode according to the pronunciation characteristic information of the user to be detected. The pronunciation correction method for the user to be detected can be used for rapidly and accurately correcting the pronunciation of the user to be detected in the process of oral teaching by extracting the feature information irrelevant to the speaker from the voice information of the user to be detected, obtaining the pronunciation feature information of any user to be detected according to the feature information irrelevant to the speaker and determining the pronunciation correction mode for the user to be detected according to the pronunciation feature information.

Description

Pronunciation correction method and device, electronic equipment and storage medium
Technical Field
The embodiment of the disclosure relates to the technical field of data processing, and in particular, to a pronunciation correction method, a pronunciation correction device, an electronic device and a storage medium.
Background
As an important medium for interpersonal communication, spoken language plays an important role in real life, and in the field of spoken language teaching, a teacher usually needs to correct spoken pronunciation of a student to improve the correctness of spoken language expression of the student.
However, since the spoken language pronunciation process of students usually involves the synergistic effect of multiple organs, pronunciation scoring evaluation is usually performed through spoken language evaluation at present to give a parameter score, but when the reason of the pronunciation error is specifically found, the reason of the pronunciation error is difficult to find out unless an extremely experienced professional teacher because the pronunciation organ is in the oral cavity, but the way not only needs more energy of the teacher, but also obviously reduces the efficiency of the spoken language teaching.
Disclosure of Invention
The embodiment of the disclosure provides a pronunciation correction method, a pronunciation correction device, an electronic device and a storage medium, so as to realize quick and accurate pronunciation correction.
In a first aspect, an embodiment of the present disclosure provides a pronunciation correction method, including: extracting feature information irrelevant to a speaker from voice information of a user to be tested;
obtaining pronunciation characteristic information of a user to be tested according to the characteristic information of the user to be tested, which is irrelevant to a speaker;
and determining a pronunciation correction mode according to the pronunciation characteristic information of the user to be detected.
In a second aspect, an embodiment of the present disclosure further provides a pronunciation correction device, including:
the characteristic information extraction module which is irrelevant to the speaker is used for extracting characteristic information which is irrelevant to the speaker from the voice information of the user to be tested;
the pronunciation characteristic information acquisition module is used for acquiring pronunciation characteristic information of the user to be tested according to the characteristic information of the user to be tested, which is irrelevant to the speaker;
and the pronunciation correction mode determining module is used for determining a pronunciation correction mode according to the pronunciation characteristic information of the user to be tested.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, where the electronic device includes:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement a method according to any embodiment of the present disclosure.
In a fourth aspect, embodiments of the present disclosure provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements a method according to any of the embodiments of the present disclosure.
In the embodiment of the disclosure, the characteristic information irrelevant to the speaker is extracted from the voice information of the user to be detected, the pronunciation characteristic information of any user to be detected can be obtained according to the characteristic information irrelevant to the speaker, and the pronunciation correction mode for the user to be detected is determined according to the pronunciation characteristic information, so that the quick and accurate pronunciation correction can be realized in the process of spoken language teaching for any user to be detected.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
Fig. 1 is a flowchart of a pronunciation correction method provided in an embodiment of the present disclosure;
FIG. 2 is a flowchart of a pronunciation correction method provided in the second embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a pronunciation correction device according to a third embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more complete and thorough understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Example one
Fig. 1 is a flowchart of a pronunciation correction method provided in an embodiment of the present disclosure, where the present embodiment is applicable to a case of correcting pronunciation of a user to be tested, and the method may be executed by a pronunciation correction apparatus provided in an embodiment of the present disclosure, and the apparatus may be implemented in a software and/or hardware manner, and may be generally integrated in a computer device. The method of the embodiment of the disclosure specifically comprises the following steps:
optionally, as shown in fig. 1, the method in the embodiment of the present disclosure may include the following steps:
step 101, extracting feature information irrelevant to a speaker from voice information of a user to be tested.
Optionally, extracting feature information irrelevant to the speaker from the voice information of the user to be tested may include: inputting the voice information of the user to be tested into an automatic voice recognition ASR model irrelevant to a speaker; and extracting the PPG from the voice information through an ASR model, and taking the PPG as the characteristic information which is irrelevant to the speaker.
Specifically speaking, in the process of oral teaching of the user to be detected, the user to be detected can read in sequence according to characters written in advance, in the process of reading of the user to be detected, the voice of the user to be detected is collected in real time by the recording device, the collected voice in the appointed time range is intercepted and evaluated, and the intercepted voice is used as voice information of the user to be detected.
The feature information irrelevant to the speaker in the embodiment may be, specifically, a posterior probability of Speech (PPG), and may be extracted by an Automatic Speech Recognition (ASR) model irrelevant to the speaker. In the process of recognizing and converting the voice information of the user to be tested into text, the ASR model irrelevant to the speaker extracts PPG in the voice information, wherein the PPG contains a value set corresponding to a time range and a voice category range, and acoustic elements such as audio, tone and the like are not contained in the PPG, so that the PPG extracted by the ASR model is irrelevant to the speaker. For example, if the user a and the user b read the same word, the extracted PPG is the same, regardless of the speaker, and only determined by whether the content read by the different users is the same. Since specific characteristics and an extraction method of the PPG are not important in the present application, they are not described in detail in this embodiment.
And 102, acquiring pronunciation characteristic information of the user to be tested according to the characteristic information of the user to be tested, which is irrelevant to the speaker.
Optionally, the pronunciation feature information of the user to be detected is characterized by an Electromagnetic sounding action scanner (EMA).
Optionally, obtaining the pronunciation feature information of the user to be tested according to the feature information of the user to be tested, which is irrelevant to the speaker, may include: inputting the speaker-independent feature information of the user to be tested into a pre-trained EMA prediction model of the electromagnetic sounding action scanner, and obtaining the prediction EMA feature of the user to be tested.
Optionally, before inputting the feature information of the user to be tested, which is irrelevant to the speaker, into the pre-trained EMA prediction model of the electromagnetic sounding action scanner to obtain the predicted EMA feature of the user to be tested, the method may further include: acquiring a sample containing feature information and EMA features which are irrelevant to a speaker; and training the EMA prediction model through the samples to determine the mapping parameters of feature information irrelevant to the speaker and the EMA features in the EMA prediction model.
Optionally, the inputting the feature information of the user to be tested, which is irrelevant to the speaker, into the pre-trained EMA prediction model of the electromagnetic sounding action scanner to obtain the predicted EMA feature of the user to be tested may include: inputting the characteristic information of the user to be tested, which is irrelevant to the speaker, into a pre-trained EMA prediction model; and obtaining the predicted EMA characteristics of the user to be detected through the mapping parameters.
Optionally, the EMA features include: upper lip position information, lower lip position information, chin position information, and/or tongue position information.
Before formal testing is carried out on a user to be tested, a sample containing PPG (photoplethysmography) and EMA (electro-magnetic field) characteristics irrelevant to a speaker is obtained firstly. The method for obtaining the sample may specifically be that six electrodes of the electromagnetic sound-generating motion scanner are respectively fixed at six positions of the teacher's upper lip, lower lip, chin, tongue front end, tongue middle part, and tongue rear end, wherein a user may select the specific positions of the front end, the middle part, and the rear end of the tongue according to actual conditions, and the method is not strictly limited in this embodiment. In the process that a teacher reads according to written characters in advance in sequence, the position information of the upper lip, the position information of the lower lip, the position information of the chin and the position information of the tongue of the teacher are respectively collected through six electrodes, each position information is two-dimensional data, namely the position information comprises a horizontal coordinate and a vertical coordinate, and therefore 6 pieces of two-dimensional data are obtained as EMA characteristics. Meanwhile, in the process of reading characters by a teacher, PPG of the voice uttered by the teacher is extracted through an ASR model irrelevant to the speaker, a sample containing PPG and EMA characteristics is obtained for each pronunciation, the required number of samples can be obtained through reading different texts by the teacher, and the EMA prediction model is trained through the obtained samples, so that mapping parameters of feature information irrelevant to the speaker in the EMA prediction model and the EMA characteristics are determined.
Specifically, after the training of the EMA prediction model is completed, in the process of teaching students, no matter the requirements of cost limitation or teaching comfort level, the electrodes for acquiring the EMA characteristics cannot be worn by each student. Therefore, in the process of pronunciation correction teaching of students, when a student a reads according to a given text, PPG extracted by an ASR model irrelevant to a speaker can be input into an EMA prediction model which is pre-selected and trained, and the EMA prediction model can output the predicted EMA characteristic of the student a according to mapping parameters obtained in training. For example, deriving a predicted EMA characteristic for student a includes: upper lip position information (x1, y1), lower lip position information (x2, y2), chin position information (x3, y3), tongue tip position information (x4, y4), tongue middle position information (x5, y5), and tongue rear position information (x6, y 6).
It should be noted that, since PPG is speaker-independent, the EMA prediction model is trained only according to teacher data, but may predict EMA characteristics of different students during application.
And 103, determining a pronunciation correction mode according to the pronunciation characteristic information of the user to be detected.
Optionally, when the pronunciation feature information of the user to be detected is characterized by the EMA feature of the electromagnetic sounding action scanner, determining a pronunciation correction mode according to the pronunciation feature information of the user to be detected may include: determining pronunciation correction mode according to predicted EMA characteristics
Optionally, determining a pronunciation correction mode according to the predicted EMA characteristics may include: acquiring a pronunciation rule corresponding to the voice information, wherein the pronunciation rule comprises a standard EMA characteristic corresponding to the voice information under the condition of pronunciation standard; and comparing the predicted EMA features with the standard EMA features, and determining a pronunciation correction mode according to the comparison result.
Optionally, comparing the predicted EMA feature with the standard EMA feature, and determining a pronunciation correction manner according to the comparison result, may include: comparing the predicted EMA characteristics with standard EMA characteristics, and determining difference position information of which the difference value exceeds a preset threshold value according to a comparison result; and determining a pronunciation correction mode according to the difference position information.
Specifically, in this embodiment, a pronunciation rule corresponding to the speech information is further obtained, and the pronunciation rule includes an EMA characteristic corresponding to the speech information under the condition of pronunciation criteria, for example, when the student a reads a specified text, the speech information corresponding to the text includes a phoneme "o", and the standard EMA characteristic corresponding to the speech information phoneme "o" includes: upper lip position information (x 1)、,y1、) Lower lip position information (x)、,y2、) Chin position information (x 3)、,y3、) Tongue tip position information (x 4)、,y4、) Tongue middle position information (x 5)、,y5、) And tongue posterior position information (x 6)、,y6、). Meanwhile, when the student a reads the specified text, the predicted EMA features obtained by the EMA prediction model comprise: upper lip position information (x1, y1), lower lip position information (x2, y2), chin position information (x3, y3), tongue tip position information (x4, y4), tongue middle position information (x5, y5), and tongue rear position information (x6, y 6). And comparing the predicted EMA features with the standard EMA features, determining difference position information with a difference value exceeding a preset threshold value according to a comparison result, and determining a pronunciation correction mode according to the difference position information.
Wherein the tongue front position information (x4, y4) and (x 4) are determined according to the comparison result、,y4、) Exceeds a predetermined threshold, in particular y4 and y4、Exceeds a preset threshold value, and y4 is less than y4、Determining the front end of the tongue as the difference position information, and determining the pronunciation correction mode as follows: the front end of the tongue is lifted upwards. Of course, in the present embodiment, only the tongue tip is taken as an example of the distinctive position information, and the specific content of the distinctive position information is not limited in the present embodiment.
In the embodiment of the disclosure, the characteristic information irrelevant to the speaker is extracted from the voice information of the user to be detected, the pronunciation characteristic information of any user to be detected can be obtained according to the characteristic information irrelevant to the speaker, and the pronunciation correction mode for the user to be detected is determined according to the pronunciation characteristic information, so that the quick and accurate pronunciation correction can be realized in the process of spoken language teaching for any user to be detected.
Example two
Fig. 2 is a flowchart of a pronunciation correction method provided in the second embodiment of the present disclosure, which may be combined with various alternatives in the foregoing embodiments, and in the second embodiment of the present disclosure, after identifying and determining a pronunciation correction manner for face image information, the method further includes: and detecting the pronunciation correction mode, and giving an alarm prompt under the condition that the pronunciation correction mode is determined to be wrong according to the detection result.
As shown in fig. 2, the method of the embodiment of the present disclosure specifically includes:
step 201, extracting feature information irrelevant to a speaker from voice information of a user to be tested.
Optionally, extracting feature information irrelevant to the speaker from the voice information of the user to be tested may include: inputting the voice information of the user to be tested into an automatic voice recognition ASR model irrelevant to a speaker; and extracting the PPG from the voice information through an ASR model, and taking the PPG as the characteristic information which is irrelevant to the speaker.
Step 202, obtaining pronunciation feature information of the user to be tested according to the feature information of the user to be tested, which is irrelevant to the speaker.
Optionally, the pronunciation feature information of the user to be detected is characterized by an Electromagnetic sounding action scanner (EMA).
Optionally, obtaining the pronunciation feature information of the user to be tested according to the feature information of the user to be tested, which is irrelevant to the speaker, may include: inputting the speaker-independent feature information of the user to be tested into a pre-trained EMA prediction model of the electromagnetic sounding action scanner, and obtaining the prediction EMA feature of the user to be tested.
Optionally, before inputting the feature information of the user to be tested, which is irrelevant to the speaker, into the pre-trained EMA prediction model of the electromagnetic sounding action scanner to obtain the predicted EMA feature of the user to be tested, the method may further include: acquiring a sample containing feature information and EMA features which are irrelevant to a speaker; and training the EMA prediction model through the samples to determine the mapping parameters of feature information irrelevant to the speaker and the EMA features in the EMA prediction model.
Optionally, inputting the feature information of the user to be tested into a pre-trained electromagnetic acoustic action scanner EMA prediction model to obtain the predicted EMA feature of the user to be tested, where the obtaining may include: inputting the characteristic information of a user to be tested into a pre-trained EMA prediction model; and obtaining the predicted EMA characteristics of the user to be detected through the mapping parameters.
Optional EMA features include: upper lip position information, lower lip position information, chin position information, and/or tongue position information.
Step 203, determining a pronunciation correction mode according to the pronunciation characteristic information of the user to be tested.
Optionally, when the pronunciation feature information of the user to be detected is characterized by the EMA feature of the electromagnetic sound action scanner, determining a pronunciation correction mode according to the pronunciation feature information of the user to be detected may include: and determining a pronunciation correction mode according to the predicted EMA characteristics.
Optionally, determining a pronunciation correction mode according to the predicted EMA characteristics may include: acquiring a pronunciation rule corresponding to the voice information, wherein the pronunciation rule comprises a standard EMA characteristic corresponding to the voice information under the condition of pronunciation standard; and comparing the predicted EMA features with the standard EMA features, and determining a pronunciation correction mode according to the comparison result.
Optionally, comparing the predicted EMA feature with the standard EMA feature, and determining a pronunciation correction manner according to the comparison result, may include: comparing the predicted EMA characteristics with standard EMA characteristics, and determining difference position information of which the difference value exceeds a preset threshold value according to a comparison result; and determining a pronunciation correction mode according to the difference position information.
Step 204, detecting a pronunciation correction mode; and carrying out alarm prompt under the condition that the pronunciation correction mode is determined to be wrong according to the detection result.
After the pronunciation correction mode is obtained, the determined pronunciation correction mode is detected, and if obvious errors or invalidity exist in the determined pronunciation correction mode, an alarm is given. For example, the upper lip and the lower lip of the user to be measured are in a closed state, but the pronunciation correction mode requires that the position of the lower lip is adjusted upwards to reduce the distance between the upper lip and the lower lip, which is obviously inconsistent with the actual situation, so that the determined pronunciation correction mode is the situation that obvious errors exist. And giving an alarm prompt under the condition that the obvious error or the ineffectiveness occurs so as to prompt a tester to overhaul the equipment or the EMA prediction model in time.
In the embodiment of the disclosure, the characteristic information irrelevant to the speaker is extracted from the voice information of the user to be detected, the pronunciation characteristic information of any user to be detected can be obtained according to the characteristic information irrelevant to the speaker, and the pronunciation correction mode for the user to be detected is determined according to the pronunciation characteristic information, so that the quick and accurate pronunciation correction can be realized in the process of spoken language teaching for any user to be detected. And the pronunciation correction mode is detected, and alarm prompt is carried out under the condition that the pronunciation correction mode is wrong is determined according to the detection result, so that a tester is instructed to overhaul the equipment or the evaluation flow in time according to the alarm prompt, and the accuracy of the pronunciation correction mode is ensured.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a pronunciation correction device according to a third embodiment of the present disclosure. The apparatus may be implemented in software and/or hardware and may generally be integrated in an electronic device performing the method. As shown in fig. 3, the apparatus may include:
a speaker-independent feature information extraction module 310, configured to extract feature information that is independent of a speaker from voice information of a user to be tested;
the pronunciation characteristic information acquisition module 320 is used for acquiring pronunciation characteristic information of the user to be tested according to the characteristic information of the user to be tested, which is irrelevant to the speaker;
the pronunciation correction mode determining module 330 is configured to determine a pronunciation correction mode according to the pronunciation feature information of the user to be tested.
In the embodiment of the disclosure, the characteristic information irrelevant to the speaker is extracted from the voice information of the user to be detected, the pronunciation characteristic information of any user to be detected can be obtained according to the characteristic information irrelevant to the speaker, and the pronunciation correction mode for the user to be detected is determined according to the pronunciation characteristic information, so that the pronunciation correction can be rapidly and accurately realized in the process of oral teaching for any user to be detected.
Optionally, on the basis of the technical scheme, the pronunciation feature information of the user to be tested is characterized by an EMA feature of the electromagnetic sounding action scanner.
Optionally, on the basis of the above technical solution, the pronunciation feature information obtaining module includes a prediction EMA feature obtaining module, configured to: inputting the speaker-independent feature information of the user to be tested into a pre-trained EMA prediction model of the electromagnetic sounding action scanner to obtain the predicted EMA feature of the user to be tested;
the pronunciation correction mode determination module is used for: and determining a pronunciation correction mode according to the predicted EMA characteristics.
Optionally, on the basis of the above technical solution, the feature information extraction module unrelated to the speaker is configured to:
inputting the voice information of the user to be tested into an automatic voice recognition ASR model irrelevant to a speaker;
and extracting the PPG from the voice information through an ASR model, and taking the PPG as the feature information irrelevant to the speaker.
Optionally, on the basis of the above technical solution, the apparatus further includes:
the EMA prediction model training module is used for acquiring a sample containing feature information irrelevant to a speaker and EMA features;
and training the EMA prediction model through the sample to determine the mapping parameters of feature information irrelevant to the speaker and the EMA features in the EMA prediction model.
Optionally, on the basis of the above technical solution, the predicted EMA feature acquisition module is configured to: inputting the characteristic information of the user to be tested, which is irrelevant to the speaker, into a pre-trained EMA prediction model;
and obtaining the predicted EMA characteristics of the user to be detected through the mapping parameters.
Optionally, on the basis of the above technical solution, the pronunciation correction mode determining module includes:
the pronunciation rule acquisition submodule is used for acquiring a pronunciation rule corresponding to the voice information, wherein the pronunciation rule comprises a standard EMA characteristic corresponding to the voice information under the condition of pronunciation standard;
and the pronunciation correction mode determining submodule is used for comparing the predicted EMA characteristics with the standard EMA characteristics and determining a pronunciation correction mode according to a comparison result.
Optionally, on the basis of the above technical solution, the EMA features include: upper lip position information, lower lip position information, chin position information, and/or tongue position information.
Optionally, on the basis of the above technical solution, the pronunciation correction mode determination submodule is further configured to:
comparing the predicted EMA characteristics with standard EMA characteristics, and determining difference position information of which the difference value exceeds a preset threshold value according to a comparison result;
and determining a pronunciation correction mode according to the difference position information.
The pronunciation correcting device provided by the embodiment of the present disclosure is the same as the pronunciation correcting method provided by the embodiments, and the technical details not described in detail in the embodiment of the present disclosure can be referred to the embodiments, and the embodiment of the present disclosure has the same beneficial effects as the embodiments.
Example four
Referring now to FIG. 4, a block diagram of an electronic device 400 suitable for use in implementing embodiments of the present disclosure is shown. The electronic device in the embodiment of the present disclosure may be a device corresponding to a backend service platform of an application program, and may also be a mobile terminal device installed with an application program client. In particular, the electronic device may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle-mounted terminal (e.g., a car navigation terminal), etc., and a stationary terminal such as a digital TV, a desktop computer, etc. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, electronic device 400 may include a processing device (e.g., central processing unit, graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage device 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device 400 to communicate wirelessly or by wire with other devices to exchange data. While fig. 4 illustrates an electronic device 400 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may be alternatively implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication device 409, or from the storage device 408, or from the ROM 402. The computer program performs the above-described functions defined in the methods of the embodiments of the present disclosure when executed by the processing device 401.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the internal processes of the electronic device to perform: extracting feature information irrelevant to a speaker from voice information of a user to be detected; inputting the characteristic information of the user to be tested into a pre-trained EMA prediction model of the electromagnetic sounding action scanner to obtain the predicted EMA characteristic of the user to be tested; and determining a pronunciation correction mode according to the predicted EMA characteristics.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, including conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
According to one or more embodiments of the present disclosure, [ example 1 ] there is provided a pronunciation correction method comprising:
extracting feature information irrelevant to a speaker from voice information of a user to be detected;
obtaining pronunciation characteristic information of the user to be tested according to the characteristic information of the user to be tested, which is irrelevant to the speaker;
and determining a pronunciation correction mode according to the pronunciation characteristic information of the user to be detected.
In accordance with one or more embodiments of the present disclosure, [ example 2 ] there is provided the method of example 1, further comprising: and the pronunciation characteristic information of the user to be detected is characterized by an EMA (electromagnetic acoustic action scanner) characteristic.
In accordance with one or more embodiments of the present disclosure, [ example 3 ] there is provided the method of example 2, further comprising:
inputting the speaker-independent feature information of the user to be tested into a pre-trained EMA prediction model of an electromagnetic sounding action scanner to obtain the predicted EMA feature of the user to be tested;
the determining of the pronunciation correction mode according to the pronunciation feature information of the user to be tested comprises:
and determining a pronunciation correction mode according to the predicted EMA characteristics.
In accordance with one or more embodiments of the present disclosure, [ example 4 ] there is provided the method of example 1, further comprising:
inputting the voice information of the user to be tested into an automatic voice recognition ASR model irrelevant to a speaker;
and extracting the PPG from the voice information through the ASR model, and taking the PPG as the feature information which is irrelevant to the speaker.
According to one or more embodiments of the present disclosure, [ example 5 ] there is provided the method of example 3, further comprising:
acquiring a sample containing feature information and EMA features which are irrelevant to a speaker;
and training the EMA prediction model through the sample to determine mapping parameters of feature information and EMA features which are irrelevant to a speaker in the EMA prediction model.
According to one or more embodiments of the present disclosure, [ example 6 ] there is provided the method of example 5, further comprising:
inputting the speaker-independent feature information of the user to be tested into the EMA prediction model trained in advance;
and obtaining the predicted EMA characteristics of the user to be detected through the mapping parameters.
In accordance with one or more embodiments of the present disclosure, [ example 7 ] there is provided the method of example 3, further comprising:
acquiring a pronunciation rule corresponding to the voice information, wherein the pronunciation rule comprises a standard EMA characteristic corresponding to the voice information under the condition of pronunciation standard;
and comparing the predicted EMA characteristics with standard EMA characteristics, and determining a pronunciation correction mode according to a comparison result.
According to one or more embodiments of the present disclosure, [ example 8 ] there is provided the method of example 7, the EMA feature comprising: upper lip position information, lower lip position information, chin position information, and/or tongue position information.
According to one or more embodiments of the present disclosure, [ example 9 ] there is provided the method of example 8, further comprising:
comparing the predicted EMA features with standard EMA features, and determining difference position information of which the difference value exceeds a preset threshold value according to a comparison result;
and determining a pronunciation correction mode according to the difference position information.
According to one or more embodiments of the present disclosure, [ example 10 ] there is provided a pronunciation correction device comprising:
the characteristic information extraction module which is irrelevant to the speaker is used for extracting characteristic information which is irrelevant to the speaker from the voice information of the user to be tested;
the pronunciation characteristic information acquisition module is used for acquiring pronunciation characteristic information of the user to be tested according to the characteristic information of the user to be tested, which is irrelevant to the speaker;
and the pronunciation correction mode determining module is used for determining a pronunciation correction mode according to the pronunciation characteristic information of the user to be tested.
According to one or more embodiments of the present disclosure, [ example 11 ] there is provided the apparatus of example 10, the vocal feature information of the user under test is characterized by an electromagnetic vocal action scanner EMA feature.
According to one or more embodiments of the present disclosure, [ example 12 ] there is provided the apparatus of example 11, the pronunciation feature information acquisition module comprising a predictive EMA feature acquisition module to: inputting the speaker-independent feature information of the user to be tested into a pre-trained EMA prediction model of an electromagnetic sounding action scanner to obtain the predicted EMA feature of the user to be tested;
the pronunciation correction mode determination module is used for: and determining a pronunciation correction mode according to the predicted EMA characteristics.
According to one or more embodiments of the present disclosure, [ example 13 ] there is provided the apparatus of example 10, the speaker-independent feature information extraction module to:
inputting the voice information of the user to be tested into an automatic voice recognition ASR model irrelevant to a speaker;
and extracting the PPG from the voice information through the ASR model, and taking the PPG as the feature information which is irrelevant to the speaker.
According to one or more embodiments of the present disclosure, [ example 14 ] there is provided the apparatus of example 12, further comprising:
the EMA prediction model training module is used for acquiring a sample containing feature information irrelevant to a speaker and EMA features;
and training the EMA prediction model through the sample to determine mapping parameters of feature information and EMA features which are irrelevant to a speaker in the EMA prediction model.
According to one or more embodiments of the present disclosure, [ example 15 ] there is provided the apparatus of example 14, the predicted EMA feature acquisition module to:
inputting the speaker-independent feature information of the user to be tested into the EMA prediction model trained in advance;
and obtaining the predicted EMA characteristics of the user to be detected through the mapping parameters.
According to one or more embodiments of the present disclosure, [ example 16 ] there is provided the apparatus of example 12, the pronunciation correction manner determination module comprising:
a pronunciation rule obtaining submodule, configured to obtain a pronunciation rule corresponding to the voice information, where the pronunciation rule includes a standard EMA characteristic corresponding to the voice information under a pronunciation standard;
and the pronunciation correction mode determining submodule is used for comparing the predicted EMA characteristics with the standard EMA characteristics and determining a pronunciation correction mode according to a comparison result.
According to one or more embodiments of the present disclosure, [ example 17 ] there is provided the apparatus of example 16: upper lip position information, lower lip position information, chin position information, and/or tongue position information.
According to one or more embodiments of the present disclosure, [ example 18 ] there is provided the apparatus of example 17, the pronunciation correction mode determination submodule to: comparing the predicted EMA features with standard EMA features, and determining difference position information of which the difference value exceeds a preset threshold value according to a comparison result;
and determining a pronunciation correction mode according to the difference position information.
According to one or more embodiments of the present disclosure, [ example 19 ] there is provided an electronic device comprising a memory, a processing means and a computer program stored on the memory and executable on the processing means, characterized in that the processing means when executing the program implements the method according to any of examples 1-9.
According to one or more embodiments of the present disclosure, [ example 20 ] there is provided a storage medium containing computer-executable instructions for performing the method of any of examples 1-9 when executed by a computer processor.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims (10)

1. A pronunciation correction method, comprising:
extracting feature information irrelevant to a speaker from voice information of a user to be tested;
obtaining pronunciation characteristic information of the user to be tested according to the characteristic information of the user to be tested, wherein the pronunciation characteristic information of the user to be tested is characterized by an EMA (electromagnetic acoustic action scanner);
determining a pronunciation correction mode according to the pronunciation characteristic information of the user to be detected;
the obtaining of the pronunciation feature information of the user to be tested according to the feature information of the user to be tested, which is irrelevant to the speaker, includes: inputting the speaker-independent feature information of the user to be tested into a pre-trained EMA prediction model of an electromagnetic sounding action scanner to obtain the predicted EMA feature of the user to be tested;
the method for extracting the feature information irrelevant to the speaker from the voice information of the user to be tested comprises the following steps: inputting the voice information of the user to be tested into an automatic voice recognition ASR model irrelevant to a speaker; and extracting the PPG from the voice information through the ASR model, and taking the PPG as the feature information which is irrelevant to the speaker.
2. The method according to claim 1, wherein the determining a pronunciation correction manner according to the pronunciation feature information of the user to be tested comprises:
and determining a pronunciation correction mode according to the predicted EMA characteristics.
3. The method of claim 1, wherein before inputting the speaker-independent feature information of a user to be tested into a pre-trained EMA prediction model of an electromagnetic sound action scanner (EMA) to obtain a predicted EMA feature of the user to be tested, the method further comprises:
acquiring a sample containing speaker-independent feature information and EMA features;
and training the EMA prediction model through the sample to determine mapping parameters of feature information and EMA features which are irrelevant to a speaker in the EMA prediction model.
4. The method of claim 3, wherein the inputting the speaker-independent feature information of the user to be tested into a pre-trained EMA prediction model of an electromagnetic voice activity scanner (EMA) to obtain the predicted EMA feature of the user to be tested comprises:
inputting the speaker-independent feature information of the user to be tested into the EMA prediction model trained in advance;
and obtaining the predicted EMA characteristics of the user to be detected through the mapping parameters.
5. The method of claim 2, wherein determining a pronunciation correction by the predicted EMA features comprises:
acquiring a pronunciation rule corresponding to the voice information, wherein the pronunciation rule comprises a standard EMA characteristic corresponding to the voice information under the condition of pronunciation standard;
and comparing the predicted EMA features with standard EMA features, and determining a pronunciation correction mode according to a comparison result.
6. The method of claim 5, wherein the EMA features comprise: upper lip position information, lower lip position information, chin position information, and/or tongue position information.
7. The method according to claim 6, wherein comparing the predicted EMA features with standard EMA features and determining pronunciation correction based on the comparison comprises:
comparing the predicted EMA features with standard EMA features, and determining difference position information of which the difference value exceeds a preset threshold value according to a comparison result;
and determining a pronunciation correction mode according to the difference position information.
8. A pronunciation correction device, comprising:
the characteristic information extraction module which is irrelevant to the speaker is used for extracting characteristic information which is irrelevant to the speaker from the voice information of the user to be tested;
the pronunciation characteristic information acquisition module is used for acquiring pronunciation characteristic information of the user to be detected according to the characteristic information of the user to be detected, wherein the pronunciation characteristic information of the user to be detected is characterized by an EMA (electromagnetic acoustic action scanner);
the pronunciation correction mode determining module is used for determining a pronunciation correction mode according to the pronunciation characteristic information of the user to be tested;
the pronunciation characteristic information acquisition module is used for inputting the speaker-independent characteristic information of the user to be tested into a pre-trained EMA prediction model of the electromagnetic pronunciation action scanner to acquire the predicted EMA characteristic of the user to be tested;
the speaker-independent feature information extraction module is used for inputting the voice information of the user to be tested into an automatic voice recognition ASR model which is independent of the speaker; and extracting the PPG from the voice information through the ASR model, and taking the PPG as the feature information which is irrelevant to the speaker.
9. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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